Economic and Energetic Performance Evaluation of Unreliable Production Lines: An Integrated Analytical Approach

Abstract

The current context is increasingly driving researchers and industry to focus not only on the economic but also on the energetic performance of manufacturing systems. However, considerable work on the enhancement of energetic performance of complex production systems is still needed. This paper addresses a novel integrated analytical method to evaluate simultaneously the economic and energetic performances of a serial production line composed of unreliable machines and intermediate buffers. This approach is based on a discrete Markov chain formulation of machines states transitions and a birth-death Markov process for buffers states evaluation. It introduces throughput, energy consumption and energy efficiency as key performance indicators for assessing economic and energetic performances. Structural characteristics of the problem are analyzed to establish and evaluate the impact of buffers size, reliability parameters, and production rates of the machines on the energetic performance of the production line. A large experimental study, based on different instances inspired by the literature, is carried out to analyze the behavior and the complex trade-off between throughput and energy efficiency performances.

Multi-Objective Optimization of Energy-Efficient Buffer Allocation Problem for Non-Homogeneous Unreliable Production Lines

“…The evaluation approach of the two crucial performances considered in the problem, i.e. throughput and energy consumption of the line, was developed in [40]. The throughput is evaluated using the Equivalent Machine Method [41].…”

“…Corresponding formulations for steady state probabilities P l,i l ∈= {1...5} i ∈ {1...K} and energy consumption E i for each machine M i i ∈ {1...K} as well as the total energy consumption of the line E are given by equations 12-20 respectively. Further details can be found in [40].…”

“…The multi-objective EE-BAP described in equation 21 is then solved using WSM, ECM, and NSGA-II. The classical methods WSM and ECM rely on the resolution of single objective EE-BAP using the MINLP in [11], whereas the NSGA-II requires the performance evaluation method developed in [40].…”

“…Using the NSGA-II, the algorithm has a computational complexity of O(M N 2 ), with M representing the number of objectives and N the size of the population. The fitness function uses the performance evaluation method in [40]. This method reduces drastically the computational complexity for evaluating throughput when compared to other evaluation methods from the literature such as decomposition or aggregation.…”

“…For solving the single objective BAP in WSM and ECM, we use an integrated optimization method formalizing the problem into a mathematical programming model, in which the performance evaluation method [40] is included. The obtained MINLP is solved using a mathematical solver, as in [11] where it demonstrated high accuracy with low computational time.…”